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. 2016 Oct 7;11(10):e0164095.
doi: 10.1371/journal.pone.0164095. eCollection 2016.

Optical Coherence Tomography in the UK Biobank Study - Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies

Pearse A Keane  1 , Carlota M Grossi  2 , Paul J Foster  1   2 , Qi Yang  3 , Charles A Reisman  3 , Kinpui Chan  3 , Tunde Peto  1 , Dhanes Thomas  1 , Praveen J Patel  1 ; UK Biobank Eye Vision Consortium
Affiliations

Optical Coherence Tomography in the UK Biobank Study - Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies

Pearse A Keane et al. PLoS One. .

Abstract

Purpose: To describe an approach to the use of optical coherence tomography (OCT) imaging in large, population-based studies, including methods for OCT image acquisition, storage, and the remote, rapid, automated analysis of retinal thickness.

Methods: In UK Biobank, OCT images were acquired between 2009 and 2010 using a commercially available "spectral domain" OCT device (3D OCT-1000, Topcon). Images were obtained using a raster scan protocol, 6 mm x 6 mm in area, and consisting of 128 B-scans. OCT image sets were stored on UK Biobank servers in a central repository, adjacent to high performance computers. Rapid, automated analysis of retinal thickness was performed using custom image segmentation software developed by the Topcon Advanced Biomedical Imaging Laboratory (TABIL). This software employs dual-scale gradient information to allow for automated segmentation of nine intraretinal boundaries in a rapid fashion.

Results: 67,321 participants (134,642 eyes) in UK Biobank underwent OCT imaging of both eyes as part of the ocular module. 134,611 images were successfully processed with 31 images failing segmentation analysis due to corrupted OCT files or withdrawal of subject consent for UKBB study participation. Average time taken to call up an image from the database and complete segmentation analysis was approximately 120 seconds per data set per login, and analysis of the entire dataset was completed in approximately 28 days.

Conclusions: We report an approach to the rapid, automated measurement of retinal thickness from nearly 140,000 OCT image sets from the UK Biobank. In the near future, these measurements will be publically available for utilization by researchers around the world, and thus for correlation with the wealth of other data collected in UK Biobank. The automated analysis approaches we describe may be of utility for future large population-based epidemiological studies, clinical trials, and screening programs that employ OCT imaging.

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Conflict of interest statement

Charles A. Reisman and Qi Yang are both employed by Topcon Medical Systems, Inc. Other than this employment relationship, Topcon has not funded or sponsored this research in any way and furthermore, this relationship does not affect or alter the authors' adherence to PLOS journals’ data sharing or materials policies.

Figures

Fig 1
Fig 1. Optical coherence tomography (OCT) image sets.
OCT image sets were obtained using a raster scan protocol on a spectral domain OCT system (3D OCT-1000 Mark II, Topcon, Japan).
Fig 2
Fig 2. UK Biobank Data Processing Scheme.
The source data (optical coherence tomography (OCT) image sets) were stored on a central repository and accessed via remote, secure login.
Fig 3
Fig 3. Automated Segmentation of Optical coherence tomography (OCT) image sets.
Topcon Advanced Boundary Segmentation (TABSTM) software was used to perform automated segmentation of nine intraretinal boundaries. in a rapid fashion. Boundary 7 has previously been described as the inner aspect of the photoreceptor inner segment-outer segment junction (and is still described as this by Topcon Inc.); however, in a recent proposed nomenclature for classification of retinal layers on OCT, this boundary is referred to as the photoreceptor ellipsoid zone.[22]
See this image and copyright information in PMC

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